Prioritizing App Reviews for Developer Responses on Google Play

Prioritizing App Reviews for Developer Responses on Google Play | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article href="https://arxiv.org/abs/2502.01520">Prioritizing App Reviews for Developer Responses on Google Play Mohsen Jafari, Forough Majidi, Abbas Heydarnoori This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5983680/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The number of applications in Google Play has increased dramatically in recent years. On Google Play, users can write detailed reviews and rate apps, with these ratings significantly influencing app success and download numbers. Reviews often include notable information like feature requests, which are valuable for software maintenance. Users can update their reviews and ratings anytime. Studies indicate that apps with ratings below three stars are typically avoided by potential users. Since 2013, Google Play has allowed developers to respond to user reviews, helping resolve issues and potentially boosting overall ratings and download rates. However, responding to reviews is time-consuming, and only 13% to 18% of developers engage in this practice. To address this challenge, we propose a method to prioritize reviews based on response priority. We collected and preprocessed review data, extracted both textual and semantic features, and assessed their impact on the importance of responses. We labelled reviews as requiring a response or not and trained four different machine learning models to prioritize them. We evaluated the models’ performance using metrics such as F1-Score, Accuracy, Precision, and Recall. Our findings indicate that the XGBoost model is the most effective for prioritizing reviews needing a response. Software Engineering Artificial Intelligence and Machine Learning Prioritizing App Reviews Mobile Applications Machine Learning Sentiment Analysis App Stores. Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5983680","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":412763956,"identity":"231f3593-43ce-4f2f-9ead-d5c55f7e596e","order_by":0,"name":"Mohsen Jafari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYFCCAwwfGBgk5AwgPGaitDDOAGoxJkULA0gLQ+IGorXoNh5+2PBzj0X6dv7lzyQYKqwTGwhpMTtwzLCx55lE7s4Zb8wkGM6kE6PlgPkDngMSuRtunGGTYGw7TIyW4x8b/xyQSDe4cfyZBOM/orScMWwG2pJgcL7BTIKxgTgthc0yByQMd87gMbZIOJZuTFjLjeMbG98cqJM35z/+8MaHGmtZgloYJA7AGAkMDAkElYMAP8xU/gN4VI2CUTAKRsGIBgDOd0hTVyWIzwAAAABJRU5ErkJggg==","orcid":"","institution":"Bowling Green State university","correspondingAuthor":true,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Jafari","suffix":""},{"id":412764141,"identity":"c3348829-f116-42a0-8f3e-f40c5a47d8dc","order_by":1,"name":"Forough Majidi","email":"","orcid":"","institution":"Polytechnique Montr´eal Montr´eal, Canada","correspondingAuthor":false,"prefix":"","firstName":"Forough","middleName":"","lastName":"Majidi","suffix":""},{"id":412764142,"identity":"defcd51d-443e-4865-8c50-da741dbd73b0","order_by":2,"name":"Abbas Heydarnoori","email":"","orcid":"","institution":"Bowling Green State University","correspondingAuthor":false,"prefix":"","firstName":"Abbas","middleName":"","lastName":"Heydarnoori","suffix":""}],"badges":[],"createdAt":"2025-02-07 20:08:43","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5983680/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5983680/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75974270,"identity":"d2c141ae-7d55-4656-b233-a8227dc3371d","added_by":"auto","created_at":"2025-02-11 06:19:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":213275,"visible":true,"origin":"","legend":"","description":"","filename":"mohsenjafari1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5983680/v1_covered_b7a7e0d6-809f-49d0-9cbd-9444778d9764.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003ca href=\"https://arxiv.org/abs/2502.01520\"\u003e\u003cstrong\u003ePrioritizing App Reviews for Developer Responses on Google Play\u003c/strong\u003e\u003c/a\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Bowling Green State Univeristy","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prioritizing App Reviews, Mobile Applications, Machine Learning, Sentiment Analysis, App Stores.","lastPublishedDoi":"10.21203/rs.3.rs-5983680/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5983680/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe number of applications in Google Play has increased dramatically in recent years. On Google Play, users can write detailed reviews and rate apps, with these ratings significantly influencing app success and download numbers. Reviews often include notable information like feature requests, which are valuable for software maintenance. Users can update their reviews and ratings anytime. Studies indicate that apps with ratings below three stars are typically avoided by potential users. Since 2013, Google Play has allowed developers to respond to user reviews, helping resolve issues and potentially boosting overall ratings and download rates. However, responding to reviews is time-consuming, and only 13% to 18% of developers engage in this practice. To address this challenge, we propose a method to prioritize reviews based on response priority. We collected and preprocessed review data, extracted both textual and semantic features, and assessed their impact on the importance of responses. We labelled reviews as requiring a response or not and trained four different machine learning models to prioritize them. We evaluated the models’ performance using metrics such as F1-Score, Accuracy, Precision, and Recall. 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